Many websites provide product and/or item recommendations to users based on purchases, views, and/or other online activity. For example, a website may recommend an item in a catalog to a user based on an item previously purchased by that user or another user. However, for some websites, the number of items available in the catalog may be large and consistently growing. As such, techniques for recommending items may become cumbersome and/or unmanageable. Additionally, techniques for grouping items in a catalog or determining topics for a catalog may become cumbersome and time-consuming. For example, when using latent Dirichlet allocation (“LDA”) to sort the items in a catalog, a relatively large effort may be expended on optimizing the number of topics to use for an LDA analysis. Therefore, finding improved ways to cluster, group, and/or recommend items continues to be a priority.